• We covered inductive vs deductive reasoning
• How to begin to ask questions
• Accuracy and precision
• What are general types of data
• How to set up an R project in Rstudio
• How to install and load libraries
• How to read a file into R
• How to make a graph

Our first graph

• Data management overview
• How to make a tidy spreadsheet
• Metadata - why you really should use it
• Data repositories
• R in practice

New image here

Step 1

1. Decide on what type of data you are collecting
2. Decide on controlled vocabulary - odo_mgl, drp_ugl
3. Decide on what has to happen to the data flow
4. Organize your project -
5. Enter the data as soon as you can
6. in a spreadsheet as excel and csv
7. you really need to be sure it is tidy

Tidy data by Whickham

Step 2:

1. Make a MetaData sheet a. data about data b. descriptions, units, etc.

Tidy data

Step 3

1. Store your raw data and metadata
2. Electronic dataframes should be stored in ≥3 copies:
   1. Your computer (onsite)
   2. External storage (onsite)
   3. Offsite storage (e.g., cloud storage)
3. Have regular backup strategy

Step 4

1. Graph your data and check outliers, errors, missing data
2. You can choose a NA or space… opinions differ
   1. you can set this when you import data

# Specifying NA values explicitly
data <- read_csv("your_file.csv", 
    na = c("", "NA", "N/A", "missing", "null"))

Step 5

Cleaning Data -

1. Correct erors, fill missing data with “NA”, resolve outliers
2. save a clean data file as the master file - often good to make read only
3. Add information in a notes column or text file about what was done and why.

image

Step 6

1. Time to graph the data and explore, summarize, and transform data
2. If there are a lot of steps in cleaning and doing transfomations and calculations save them to new output file.

A good way to organize script files is number them in the order they get run.

The important considerations in data

1. enter field/ lab data into electronic format as soon as possible and back it up in at least one location (e.g., cloud storage)
2. do not modify raw data in any way following entry into electronic format
3. store all data in an open-access format (e.g., .csv)
4. thoroughly check and clean your raw data, saving it as a separate file (e.g., “output/cleaned_raw_data.csv”)
5. accompany raw field/lab data with meta-data that is unambiguously linked to the raw data file
6. carry out all analyses, calculations and visualization on a separate file from the “raw“ or “clean” data: the “analysis” data
7. perform all data transformation, analysis and visualization by reproducible code and code shall be stored together with data
8. arrange all raw and analysis data in “instance-row, variable-column” or tidy format: one column per variable

USE CONTROLLED VOCABULARY AND BE CONSISTENT THINK BEFORE DOING –> WHAT HAPPENS DOWN THE ROAD

Broman KW, & Woo KH. 2018. Data organization in spreadsheets. The American Statistician 72: 2-10 (HERE)

• Spreadsheets break data - use with extreme caution
• Spreadsheets: data entry and storage
• R: visualization and analysis
• Goal: organize data so readable by humans and computers